Transcript ppt
Simulations of the galaxy population constrained by observations from z=3 to present day: implications for galactic winds and fate of their ejecta Bruno Henriques Simon White, Peter Thomas, Raul Angulo, Qi Guo, Gerard Lemson, Volker Springel Motivation Why use a phenomenological approach to study galaxy formation? Because we should. The physics of galaxy formation are complex but observations suggest they must obey simple relations. Still, we do not have a good understanding and cannot work from first principles, so models must be observationally based. Fast method to compute the evolution of the galaxy population across cosmic time for samples as large as modern surveys. Guo2011 The evolution of the stellar mass function The model fits the present day distribution of masses but predicts the dwarf galaxy population to build up too early. colour Dwarf galaxies are too clustered clustering Massive galaxies are too blue and dwarf galaxies too red SSFR Dwarf galaxies are too old and are not forming enough stars 3. Self-consistent model of galaxy formation across cosmic time Henriques et al. (2009), Henriques & Thomas (2010), Henriques et al. (in prep.) Complex galaxy formation physics Large Volume Across Cosmic Time Semi-analytic modelling MCMC Robust statistical method to explore the allowed likelihood regions in highdimensional parameter spaces Choose parameters to sample Star formation, SN feedback, AGN feedback efficiency, Metals yield Constrain the model at multiple redshifts Wide and narrow surveys combined to achieve good statistics and large dynamical range. Maximum and minimum observational errors used to estimate systematic uncertainties. Stellar Mass Function, K-band & B-band Luminosity Functions Time varying parameters Reincorporation of gas after ejection by SN feedback needs to increase towards low redshift All other parameters have consistent regions at all redshifts Reincorporation time scaling with Mvir, similar to Oppenheimer et al. (2008, 2010) Strong ejection + no reincorporation set the low mass end at high-z Strong reincorporation at later times produces the required build up for z<1 Colors and SFR The delayed reincorporation of gas shifts star formation in dwarfs to lower redshifts. A population of low mass galaxies with blue colours remains down to z=0 Low mass galaxies have higher star formation rates and younger ages. Satellite galaxies in massive halos have lower mass, hence reducing clustering at fixed mass Galaxy formation physics, and not just cosmology/merging, have a strong impact on galaxy clustering. Conclusions Phenomenological models provide a fast method to describe the formation and evolution of galaxies in a cosmological volume, with high resolution and across cosmic time. MCMC methods can be used to learn exactly how specific descriptions of a physical process affect galaxy observables at different epochs in a self-consistent way. The allowed likelihood regions in parameter space can be explored for any combination of observations at multiple epochs. In order to explain the observed evolution of the number density of intermediate/low-mass galaxies, the reincorporation of ejected gas should scale approximately with Mvir, being negligible at low mass at z>2 and rapid for most galaxies at low redshift. Low-galaxies form later and are significantly younger at z=0 Evolution of the massive end is reproduced across all redshifts Luminosity Function No feedback The halo mass function is much steeper at both ends than the galaxy stellar mass function Observations low mass high mass Supernova feedback has the right scale to make star formation sufficiently inefficient in small haloes Supernova feedback The reheated gas would eventually cool in massive haloes producing an excessive number of bright galaxies Observations high mass 8 April 2015 low mass 12 Z=2.0 Z=1.0 Z=0.0 Massive galaxies have more gas fuel than small ones No ongoing star formation Older populations than small galaxies 8 April 2015 13 The Munich Model Reincorporation Hot Gas Reheating Ejected Gas Ejection Recycling Cooling Stars Stars Cold Gas Star Formation 1.The Munich Model Croton et al. 2006 AGN feedback model (suppression of cooling) De Lucia & Blaizot 2007 dust model SN feedback model - reheating + ejection + reincorporation Guo et al. 2011 different supernova feedback (increased efficiency) Merger treatment Henriques et al. 2011, 2012 different stellar populations Extended MCMC Capabilities Observational constraints at multiple redshifts Stellar mass and luminosity functions constraints from z=3 to z=0 Takes full advantage of the self-consistent evolution of galaxies Time-evolution of parameters (pre-processing step) If not needed, the current parametrisation is not ruled out by observations If needed, a different parametrisation is required (it rules out any others) If a good fit can not be found, the current model is ruled out M05 vs BC03 Gas TB-AGB TB-AGB + RHeB GALFORMOD Web-based, modeler & observer friendly semi-analytic model Combine the most robust set of dark matter numerical simulations available Stellar Mass resolution of 108M with a large enough volume to sample BAO MS, MII & MXXL Monte Carlo Markov Chain optimization + Fit physical and cosmological parameters Modular implementation of the physics “Observer friendly” outputs Choose IMF, SPS, Bands, Dust model Chemical Enrichment SN Ia + Stellar Winds 0.8 M SN II 8M Metals return timescale <100 Myr Rob Yates, Peter Thomas, Simon White, Guinevere Kauffmann, Bruno Henriques Far-Infrared Emission Peter Thomas, Sorour Shamshir, Bruno Henriques, Qi Guo + Sussex Infrared Use empirical templates from Herschel to get an emission spectra for the light re-emitted by dust Full radiative transfer code Cosmology Sampling with MCMC Bruno Henriques, Marcel Van Daalen, Raul Angulo, Simon White, Volker Springel, Fabio Fontanot, Qi Guo Incorporate the Angulo & White 2010 formalism into the semi-analytic model. Include cosmological parameters in the sampling. Colours Somerville Ages of Galaxies Light – Weighted Ages M05 Mass – Weighted Ages BC03 M05 Average!!! TP-AGB Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392) Van der Wel, Franx, Wuyts, et al. 2006 SSP Chandra Deep Field - South ACS+IRAC+J&H filters 8 April 2015 29 Maraston, Daddi, Renzini, et al. 2006 Older then the Universe! Undetected in MIPS! What are the implications for galaxy formation models? 8 April 2015 30 Marchesini 2009 Optical to mid-infrared data GOODS – Giavalisco et al. 2004 MUSYC – Gawiser et al. 2006 8 April 2015 31 CB07 M05 CB07 BC03 8 April 2015 Henriques, Maraston, Monaco, et al. (Astro-ph: 1009.1392) 32